diff --git a/ax/modelbridge/generation_node.py b/ax/modelbridge/generation_node.py
index c07c91af3ba..417065827f9 100644
--- a/ax/modelbridge/generation_node.py
+++ b/ax/modelbridge/generation_node.py
@@ -468,7 +468,7 @@ def should_transition_to_next_node(
             return True, transition_nodes[0]
         return False, None
 
-    def generator_run_limit(self) -> int:
+    def generator_run_limit(self, supress_generation_errors: bool = True) -> int:
         """How many generator runs can this generation strategy generate right now,
         assuming each one of them becomes its own trial. Only considers
         `transition_criteria` that are TrialBasedCriterion.
@@ -477,14 +477,43 @@ def generator_run_limit(self) -> int:
               - the number of generator runs that can currently be produced, with -1
                 meaning unlimited generator runs,
         """
+
+        # TODO: @mgarrard Should we consider returning `None` if there is no limit?
+        # TODO:@mgarrard Should we instead have `raise_generation_error`? The name
+        # of this method doesn't suggest that it would raise errors by default, since
+        # it's just finding out the limit according to the name. I know we want the
+        # errors in some cases, so we could call the flag `raise_error_if_cannot_gen` or
+        # something like that : )
+        trial_based_gen_blocking_criteria = [
+            criterion
+            for criterion in self.transition_criteria
+            if criterion.block_gen_if_met and isinstance(criterion, TrialBasedCriterion)
+        ]
         gen_blocking_criterion_delta_from_threshold = [
             criterion.num_till_threshold(
                 experiment=self.experiment, trials_from_node=self.trials_from_node
             )
-            for criterion in self.transition_criteria
-            if criterion.block_gen_if_met and isinstance(criterion, TrialBasedCriterion)
+            for criterion in trial_based_gen_blocking_criteria
         ]
 
+        # Raise any necessary generation errors: for any met criterion,
+        # call its `block_continued_generation_error` method The method might not
+        # raise an error, depending on its implementation on given criterion, so the
+        # error from the first met one that does block continued generation, will be
+        # raised.
+        if not supress_generation_errors:
+            for criterion in trial_based_gen_blocking_criteria:
+                # TODO[mgarrard]: Raise a group of all the errors, from each gen-
+                # blocking transition criterion.
+                if criterion.is_met(
+                    self.experiment, trials_from_node=self.trials_from_node
+                ):
+                    criterion.block_continued_generation_error(
+                        node_name=self.node_name,
+                        model_name=self.model_to_gen_from_name,
+                        experiment=self.experiment,
+                        trials_from_node=self.trials_from_node,
+                    )
         if len(gen_blocking_criterion_delta_from_threshold) == 0:
             if not self.gen_unlimited_trials:
                 logger.warning(
diff --git a/ax/modelbridge/generation_strategy.py b/ax/modelbridge/generation_strategy.py
index af2cdcfc5d2..c6cc06624b7 100644
--- a/ax/modelbridge/generation_strategy.py
+++ b/ax/modelbridge/generation_strategy.py
@@ -396,21 +396,13 @@ def _gen_multiple(
         self._maybe_move_to_next_step()
         self._fit_current_model(data=data)
 
-        # Make sure to not make too many generator runs and
-        # exceed maximum allowed paralellism for the step.
-        num_until_max_parallelism = (
-            self._curr.num_remaining_trials_until_max_parallelism()
-        )
-        if num_until_max_parallelism is not None:
-            num_generator_runs = min(num_generator_runs, num_until_max_parallelism)
-
-        # Make sure not to extend number of trials expected in step.
-        if self._curr.enforce_num_trials and self._curr.num_trials > 0:
-            num_generator_runs = min(
-                num_generator_runs,
-                self._curr.num_trials - self._curr.num_can_complete,
-            )
-
+        # Get GeneratorRun limit that respects the node's transition criterion that
+        # affect the number of generator runs that can be produced.
+        gr_limit = self._curr.generator_run_limit(supress_generation_errors=False)
+        if gr_limit == -1:
+            num_generator_runs = max(num_generator_runs, 1)
+        else:
+            num_generator_runs = max(min(num_generator_runs, gr_limit), 1)
         generator_runs = []
         pending_observations = deepcopy(pending_observations) or {}
         for _ in range(num_generator_runs):
diff --git a/ax/service/tests/test_interactive_loop.py b/ax/service/tests/test_interactive_loop.py
index 4fe08450e44..13ccc0af8c8 100644
--- a/ax/service/tests/test_interactive_loop.py
+++ b/ax/service/tests/test_interactive_loop.py
@@ -12,6 +12,8 @@
 
 import numpy as np
 from ax.core.types import TEvaluationOutcome
+from ax.modelbridge.generation_strategy import GenerationStep, GenerationStrategy
+from ax.modelbridge.registry import Models
 from ax.service.ax_client import AxClient, TParameterization
 from ax.service.interactive_loop import interactive_optimize_with_client
 from ax.utils.common.testutils import TestCase
@@ -99,7 +101,11 @@ def _elicit(
                 },
             )
 
-        ax_client = AxClient()
+        # GS with lo max parallelismm to induce MaxParallelismException:
+        generation_strategy = GenerationStrategy(
+            steps=[GenerationStep(model=Models.SOBOL, max_parallelism=1, num_trials=-1)]
+        )
+        ax_client = AxClient(generation_strategy=generation_strategy)
         ax_client.create_experiment(
             name="hartmann_test_experiment",
             # pyre-fixme[6]
@@ -116,9 +122,6 @@ def _elicit(
             minimize=True,
         )
 
-        # Lower max parallelism to induce MaxParallelismException
-        ax_client.generation_strategy._steps[0].max_parallelism = 1
-
         with self.assertLogs(logger="ax", level=WARN) as logger:
             interactive_optimize_with_client(
                 ax_client=ax_client,